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Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context

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Abstract

This paper models the complex simultaneous localization and mapping (SLAM) problem through a very flexible Markov random field and then solves it by using the iterated conditional modes algorithm. Markovian models allow to incorporate: any motion model; any observation model regardless of the type of sensor being chosen; prior information of the map through a map model; maps of diverse natures; sensor fusion weighted according to the accuracy. On the other hand, the iterated conditional modes algorithm is a probabilistic optimizer widely used for image processing which has not yet been used to solve the SLAM problem. This iterative solver has theoretical convergence regardless of the Markov random field chosen to model. Its initialization can be performed on-line and improved by parallel iterations whenever deemed appropriate. It can be used as a post-processing methodology if it is initialized with estimates obtained from another SLAM solver. The applied methodology can be easily implemented in other versions of the SLAM problem, such as the multi-robot version or the SLAM with dynamic environment. Simulations and real experiments show the flexibility and the excellent results of this proposal.

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Authors and Affiliations

  1. Automatics Institute, National University of San Juan, San Juan, 5400, Argentina

    J. Gimenez, A. Amicarelli, J. M. Toibero, F. di Sciascio & R. Carelli

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  1. J. Gimenez
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  2. A. Amicarelli
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  3. J. M. Toibero
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  4. F. di Sciascio
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  5. R. Carelli
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Corresponding author

Correspondence to J. M. Toibero.

Additional information

This work was supported by the National Council for Scientific and Technological Research (CONICET) and the National University of San Juan (UNSJ).

J. Gimenez received the B. Sc. degree in mathematics from the National University of San Juan (UNSJ),Argentina in 2009, and the Ph. D. degree in mathematics from the National University of Córdoba (UNC), Argentina in 2014. Currently, he is an assistant researcher of the Argentinean National Council for Scientific Research (CONICET), and an adjunct professor in the Institute of Automatics, Argentina

His research interests include probabilistic and statistical implementations of robotics, such as simultaneous localization and mapping (SLAM) algorithms.

A. Amicarelli received the B. Eng. degree in chemical engineering from the National University of San Juan, Argentina in 2000, and the Ph. D. degree in control systems from the Institute of Automatics (INAUT) of the same university in 2007. Her main works are related with state estimations of complex non-linear systems and with bioprocess control. She is an assistant researcher of the Argentinean National Council for Scientific Research from 2012.

Her research interests include systems modeling, process control and state estimation to control porpoises.

J. M. Toibero received the B. Eng degree in electronic engineering from the Facultad Tecnológica Nacional of Argentina, Argentina in 2002, and the Ph. D. degree in control systems from the Institute of Automatics at the National University of San Juan, Argentina in 2007. His main works are related to nonlinear control of robotic platforms and robotics applications in agriculture. He is with the National Council for Scientific and Technological Research of Argentina since 2011, actually he is an adjunct researcher. He leads different technological projects and his current scientific research is at the Institute of Automatics of San Juan, Argentina.

His research interests include wheeled mobile robots, manipulators force/impedance, switched, hybrid, nonlinear control methods applied to automatic control and visual servoing.

F. di Sciascio received the B. Sc. degree in electromechanical engineering with orientation in electronics from the National University of Buenos Aires (UBA), Argentina in 1986. He received the M. Sc. degree in engineering of control systems, and the Ph. D. degree in engineering, from the Institute of Automatics, National University of San Juan, Argentina in 1994 and 1997, respectively. Since 1987, he is a professor and researcher at the INAUT and he is currently a full professor in charge of the subjects artificial intelligence complements and identification and adaptive control in the Department of Electronics and Automation. At the same time, he is a professor of the postgraduate degree at the INAUT, Argentina.

His research interests include modelling, identification and estimation in dynamical systems, and technology developments in automatic process control.

R. Carelli received the B. Sc. degree in engineering from the National University of San Juan, Argentina in 1976, and received the Ph. D. degree in electrical engineering from the National University of Mexico (UNAM), Mexico in 1989. He is a full professor at the National University of San Juan and a senior researcher of the National Council for Scientific and Technical Research, Argentina. He is director of the Automatics Institute, National University of San Juan, Argentina. He is a senior member of IEEE and a member of Argentine Association of Automatic Control - International Federation of Automatic Control (AADECA-IFAC).

His research interests include robotics, manufacturing systems, adaptive control and artificial intelligence applied to automatic control.

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Gimenez, J., Amicarelli, A., Toibero, J.M. et al. Iterated Conditional Modes to Solve Simultaneous Localization and Mapping in Markov Random Fields Context. Int. J. Autom. Comput. 15, 310–324 (2018). https://doi.org/10.1007/s11633-017-1109-4

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